Mass spectrometry‐based protein–protein interaction networks for the study of human diseases by Alicia L Richards, Manon Eckhardt, Nevan J Krogan

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A comprehensive framework of E2–RING E3 interactions of the human ubiquitin–proteasome system by Sjoerd J L van Wijk, Sjoerd J de Vries, Patrick Kemmeren, Anding Huang, Rolf Boelens, Alexandre M J J Bonvin, H Th Marc Timmers

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“Infectious uveitis: a comprehensive systematic review of emerging trends and molecular pathogenesis using network analysis” by Muhammad Arif Asghar, Shixin Tang, Li Ping Wong, Peizeng Yang, Qinjian Zhao

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Single nucleus RNA sequencing profile analysis to reveal cell type specific common molecular drivers of Parkinson’s disease and therapeutic agents by Md. Al Amin Pappu, Md. Alamin, Most. Humaira Sultana, AKM Azad, Md. Rabiul Auwul, Sabkat Mahmud, Alvira Ajadee, Arnob Sarker, Salem A. Alyami, Md. Nurul Haque Mollah

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CEN‐tools: an integrative platform to identify the contexts of essential genes by Sumana Sharma, Cansu Dincer, Paula Weidemüller, Gavin J Wright, Evangelia Petsalaki

“…In addition, we integrate the dependency networks with existing protein–protein interaction networks to reveal context‐dependent essential cellular pathways in cancer cells. …”
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Comparative phosphoproteomics provides insights into the differences of porcine longissimus thoracis, semimembranosus, psoas major and semitendinosus muscles by Li Zhang, Wen Luo, Linlin Zhao, Longyun Li, Yizhong Huang

“…A total of 184, 53, 75, and 691 unique DAPPs were identified in LT, SMM, PS, and SMT, respectively. Protein-protein interaction networks revealed that phosphoproteins regulated meat quality differences. …”
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Identifying novel therapeutic targets for non-alcoholic fatty liver disease using bioinformatics approaches: from drug repositioning to traditional Chinese medicine by Jingmin Zhang, Tianwei Meng, Weiqi Gao, Xinghua Li, Juan Xu

“…Bioinformatics offers a promising approach to identify new targets by analyzing gene expression and drug interactions.ObjectiveThis study aims to identify novel therapeutic targets for NAFLD through bioinformatics, focusing on drug repositioning and traditional Chinese medicine (TCM) components.MethodsThree NAFLD-related gene expression datasets (GSE260666, GSE126848, GSE135251) were analyzed to identify differentially expressed genes. Protein-protein interaction networks were constructed using STRING and visualized with Cytoscape. …”
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Proteomic compensation by paralogs preserves protein interaction networks after gene loss in cancer by Anjan Venkatesh, Niall Quinn, Swathi Ramachandra Upadhya, Barbara De Kegel, Alfonso Bolado Carrancio, Thomas Lefeivre, Olivier Dennler, Kieran Wynne, Alexander von Kriegsheim, Colm J Ryan

“…Remarkably, protein–protein interaction networks operating within tumour cells continue to function despite widespread genetic perturbations. …”
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Immune molecule diagnostics in colorectal cancer: CCL2 and CXCL11 by Zhang Hui, Xu Baohong, Yin Mengqi, Dong Yan, Lu Mingliang

“…Enrichment analysis of differentially expressed genes (DEGs) was conducted using “clusterProfiler” for Gene Ontology and kyoto encyclopedia of genes and genomes pathways, identifying protein-protein interaction networks to find core hub genes. Notable differences in immune cell infiltration were observed among SSA, TSA, CRC, and healthy tissues, involving various immune cell types. …”
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Using network toxicology and molecular docking to identify core targets and pathways underlying tacrolimus-induced tremor in organ transplant recipients by Chao Liu, Qian Chen, Fu Yan, Yulin Niu

“…Targets related to tacrolimus and tremor-associated diseases were identified from public databases. Protein-protein interaction networks and functional enrichment analyses were conducted using STRING and Cytoscape. …”
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Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning by Wangnan Mao, Zhengsheng Bao, Bingbing Zhang, Lianguo Wu

“…By utilizing differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction networks, and machine learning methods, we identified three diagnostic biomarkers of OA patients. …”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

“…Using these two OA datasets and CMRGs, 21 candidate genes were identified by overlapping CMRGs and differentially expressed genes (DEGs). Protein–protein interaction networks were constructed, revealing interactions among candidate genes. …”
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Exploring the interplay between LDHA and ABCC1 in breast cancer using computational approach by Bader Alshehri

“…GeneMANIA and STRING databases were used to explore gene–gene and protein–protein interaction networks, while KM Plotter evaluated survival correlations. …”
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Multi-omics network toxicology reveals the role of benzo[a]pyrene in ovarian cancer: Integrating gut microbiota dynamics and Mendelian randomization by Jinfa Huang, Qian Yang, Lingling Zeng, Kaixian Deng

“…Target genes associated with BaP were identified using ChEMBL, PharmMapper, and GeneCards. Protein-protein interaction networks and functional enrichment analyses were conducted utilizing Cytoscape, while molecular docking (CB-Dock 2) confirmed a strong binding affinity between BaP and core targets (e.g., HSP90AA1: −11.7; AHR: −10.0). …”
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Identification of mitophagy-related biomarkers with immune cell infiltration in psoriasis by Shanshan Yu, Fangyuan Long, Hui Yan, Yongfang Xu, Jun Li, Zhimin Hao

“…Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed and protein-protein interaction networks were constructed for the functional enrichment of genes associated with mitophagy. …”
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Genetic predisposition to immune dysregulation and extracellular matrix remodeling in cardiac arrhythmia reveals potential mediation by SPP1+ macrophages by Jie-Yuan Jin, Jie-Yuan Jin, Shuai Guo, Yao Deng, Ya-Qin Chen, Chen Liang, Yu-Jie Jiang, Wang Zhao, Rong Xiang

“…The identified recurrently mutated (r.m.) genes were analyzed using protein-protein interaction networks and gene ontology enrichment for functional modules. …”
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Integrating network pharmacology and molecular modeling to decipher the anti-esophageal squamous cell carcinoma mechanisms of Bidens pilosa L by Zhenzhen Yang, Cheng Chang, Na Gao, Pan Gao, Yinsen Song, Ruiting Feng, Jingjie Meng, Chen Wang, Haozhe Zhang, Tianli Fan

“…Cross-analysis revealed 214 shared targets, with tyrosine-protein kinase, epidermal growth factor receptor, and alpha serine/threonine-protein kinase emerging as central hubs in protein-protein interaction networks. Functional enrichment analysis highlighted significant involvement in cellular responses to hormone stimuli, nitrogen compounds, and phosphorylation, with key pathways including cancer, PI3K-Akt, and Ras signaling. …”
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Activity and mechanism of naringin in the treatment of post-infectious cough by Yinghan Chen, Dong Wang, Meng Song, Weiqi Pan, Wenda Guan, Zifeng Yang, Shan Zhong

“…Venny was utilized to identify the overlapping targets between naringin and the disease. PPI (Protein–Protein Interaction) networks for disease-related targets were constructed using STRING and Cytoscape 3.10.1. …”
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Proteomic analysis of the quality deterioration of pork: a comparative study of fresh and six-month frozen storage by Yajun Shen, Chengyu Ma, Xinxiang Zong, Zhaoqiang Wang, Yang Song, Jian Li

“…Key metabolic pathways, including glycolysis, fatty acid degradation, and oxidative phosphorylation, were notably suppressed in frozen samples. Protein–protein interaction network analysis highlighted central regulatory nodes such as ATP5F1A and CAT, which were downregulated in frozen pork. …”
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Hypoxia-associated genes and metabolic abnormalities in peripheral blood mononuclear cells of type 1 diabetes mellitus patients by Wen-biao Ma, Xue-ying Wang, Yuan-yuan Zuo

“…Functional enrichment analysis, Gene Ontology analysis, and protein–protein interaction network analysis were employed to identify hub genes. …”
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